TY  - JOUR
A1  - Nathan, Ran
A1  - Monk, Christopher T.
A1  - Arlinghaus, Robert
A1  - Adam, Timo
A1  - Alós, Josep
A1  - Assaf, Michael
A1  - Baktoft, Henrik
A1  - Beardsworth, Christine E.
A1  - Bertram, Michael G.
A1  - Bijleveld, Allert
A1  - Brodin, Tomas
A1  - Brooks, Jill L.
A1  - Campos-Candela, Andrea
A1  - Cooke, Steven J.
A1  - Gjelland, Karl O.
A1  - Gupte, Pratik R.
A1  - Harel, Roi
A1  - Hellstrom, Gustav
A1  - Jeltsch, Florian
A1  - Killen, Shaun S.
A1  - Klefoth, Thomas
A1  - Langrock, Roland
A1  - Lennox, Robert J.
A1  - Lourie, Emmanuel
A1  - Madden, Joah R.
A1  - Orchan, Yotam
A1  - Pauwels, Ine S.
A1  - Riha, Milan
A1  - Röleke, Manuel
A1  - Schlägel, Ulrike
A1  - Shohami, David
A1  - Signer, Johannes
A1  - Toledo, Sivan
A1  - Vilk, Ohad
A1  - Westrelin, Samuel
A1  - Whiteside, Mark A.
A1  - Jaric, Ivan
T1  - Big-data approaches lead to an increased understanding of the ecology of animal movement
JF  - Science
N2  - Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
Y1  - 2022
U6  - https://doi.org/10.1126/science.abg1780
SN  - 0036-8075
SN  - 1095-9203
VL  - 375
IS  - 6582
SP  - 734
EP  - +
PB  - American Assoc. for the Advancement of Science
CY  - Washington
ER  - 
TY  - JOUR
A1  - Vilk, Ohad
A1  - Aghion, Erez
A1  - Nathan, Ran
A1  - Toledo, Sivan
A1  - Metzler, Ralf
A1  - Assaf, Michael
T1  - Classification of anomalous diffusion in animal movement data using power spectral analysis
JF  - Journal of physics : A, Mathematical and theoretical
N2  - The field of movement ecology has seen a rapid increase in high-resolution data in recent years, leading to the development of numerous statistical and numerical methods to analyse relocation trajectories. Data are often collected at the level of the individual and for long periods that may encompass a range of behaviours. 

Here, we use the power spectral density (PSD) to characterise the random movement patterns of a black-winged kite (Elanus caeruleus) and a white stork (Ciconia ciconia). The tracks are first segmented and clustered into different behaviours (movement modes), and for each mode we measure the PSD and the ageing properties of the process. 

For the foraging kite we find 1/f noise, previously reported in ecological systems mainly in the context of population dynamics, but not for movement data. We further suggest plausible models for each of the behavioural modes by comparing both the measured PSD exponents and the distribution of the single-trajectory PSD to known theoretical results and simulations.
KW  - diffusion
KW  - anomalous diffusion
KW  - power spectral analysis
KW  - ecological
KW  - movement data
Y1  - 2022
U6  - https://doi.org/10.1088/1751-8121/ac7e8f
SN  - 1751-8113
SN  - 1751-8121
VL  - 55
IS  - 33
PB  - IOP Publishing
CY  - Bristol
ER  -